{"id":"W2977816491","doi":"10.1016/j.colsurfb.2019.110549","title":"Self-gelling electroactive hydrogels based on chitosan–aniline oligomers/agarose for neural tissue engineering with on-demand drug release","year":2019,"lang":"en","type":"article","venue":"Colloids and Surfaces B Biointerfaces","topic":"Hydrogels: synthesis, properties, applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"Korea Institute of Materials Science","keywords":"Self-healing hydrogels; Chitosan; Agarose; Aniline; Chemistry; Neural tissue engineering; Tissue engineering; Drug delivery; Chemical engineering; Polymer chemistry; Organic chemistry; Chromatography; Biomedical engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001466494,0.0003861225,0.0001173344,0.0001900449,0.00008679354,0.0002244279,0.0002011132,0.0002261021,0.0007001421],"category_scores_gemma":[0.0001151913,0.0001540011,0.0001957854,0.0001164266,0.0001506409,0.0002647556,0.0002555316,0.0002970273,0.0002093427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002444774,"about_ca_system_score_gemma":0.000144422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004885275,"about_ca_topic_score_gemma":0.001002585,"domain_scores_codex":[0.9999121,0.00001010215,0.000006856376,0.00001901571,0.00002359312,0.0000281923],"domain_scores_gemma":[0.9998866,0.0000226315,0.0000417097,0.000009815681,0.00001561404,0.00002353553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001830461,0.000007650322,0.00002181817,0.00002277591,0.000002118888,0.00001819132,0.00001191855,0.00006168968,0.9989686,0.00005608998,0.00001532102,0.0007955153],"study_design_scores_gemma":[0.000004485046,0.00005189986,0.0003668932,0.000002758123,0.000006812151,0.00003893175,0.0000060238,0.0006166326,0.9983309,0.00001223677,0.0005579356,0.000004413407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868325,0.001315663,0.009289832,0.00006449113,0.0000313982,0.00003619056,0.0001033251,0.0001296683,0.00219696],"genre_scores_gemma":[0.9919046,0.0005656638,0.005001625,0.0000449537,0.0000069708,0.00002649015,0.00006676093,0.00002957338,0.002353351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007001421,"threshold_uncertainty_score":0.002342224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005459788577623351,"score_gpt":0.2098712284739455,"score_spread":0.2044114398963221,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}